Awesome

ipysigma

A Jupyter widget using sigma.js and graphology to render interactive networks directly within the result of a notebook cell.

Live Demo

ipysigma has been designed to work with either networkx or igraph.

ipysigma lets you customize a large number of the graph's visual variables such as: node color, size, label, border, halo, pictogram, shape and edge color, size, type, label etc.

For an exhaustive list of what visual variables you may tweak, check the "Available visual variables" part of the documentation.

ipysigma is also able to display synchronized & interactive "small multiples" of a same graph to easily compare some of its features.

Summary

Installation
Quick start
Examples
What data can be used as visual variable
Visual variables and kwargs naming rationale
Scales, palettes and gradients
Widget-side metrics
Frequently asked questions
Available visual variables
API Reference

Installation

You can install using pip:

pip install ipysigma

You will also need to install either networkx or igraph.

If you are using an older version of Jupyter, or if the extension does not appear to be installed automatically, you might also need to run some nbextension/labextension commands likewise:

# Try one/all of those for jupyter notebook:
jupyter nbextension enable --py --sys-prefix ipysigma
jupyter nbextension enable --py --user ipysigma
jupyter nbextension enable --py --system ipysigma

# Try one/all of those for jupyter lab:
jupyter labextension enable ipysigma
jupyter labextension enable ipysigma user
jupyter labextension enable ipysigma sys-prefix

If you want to use ipysigma on Google Colab, you will need to enable widget output using the following code:

from google.colab import output

output.enable_custom_widget_manager()

Remember you can always install packages in Colab by executing the following command in a cell:

!pip install networkx ipysigma

Quick start

Using networkx

import networkx as nx
from ipysigma import Sigma

# Importing a gexf graph
g = nx.read_gexf('./my-graph.gexf')

# Displaying the graph with a size mapped on degree and
# a color mapped on a categorical attribute of the nodes
Sigma(g, node_size=g.degree, node_color='category')

Using igraph

import igraph as ig
from ipysigma import Sigma

# Generating a graph
g = ig.Graph.Famous('Zachary')

# Displaying the graph with a size mapped on degree and
# a color mapped on node betweenness centrality, using
# a continuous color scale named "Viridis"
Sigma(g, node_size=g.degree, node_color=g.betweenness(), node_color_gradient='Viridis')

Examples

Compute a Louvain partition and use it as node color

ipysigma is able to compute metrics on the widget side using graphology. As such, you can ask it to compute e.g. a Louvain partitioning if you don't want or cannot do it on the python side.

For more information about available metrics and how to specify them, check this part of the documentation.

Sigma(g, node_metrics=["louvain"], node_color="louvain")

# Renaming the target attribute
Sigma(g, node_metrics={"community": "louvain"}, node_color="community")

# Passing custom parameters
Sigma(
  g,
  node_metrics={"community": {"name": "louvain", "resolution": 1.5}},
  node_color="community"
)

Use networkx/igraph/custom metrics as visual variables

Use networkx metrics:

import networkx as nx

g = nx.path_graph(5)
Sigma(g, node_size=nx.eigenvector_centrality(g))

Use igraph metrics:

import igraph as ig

g = ig.Graph.GRG(5, 0.5)
Sigma(g, node_size=g.pagerank(), node_color=g.connected_components())

Use custom metrics:

import networkx as nx

def even_or_odd(node):
  return node % 2 == 0

g = nx.path_graph(5)
Sigma(g, node_color=even_or_odd)

Read this for an exhaustive list of what can be used as visual variables.

Display a pandas DataFrame as a graph

Converting tabular data to a graph is not obvious. So for this, we advise to use helper functions found in our other library python pelote.

In this first example, we create a graph from a DataFrame of edges:

import pandas as pd
from pelote import edges_table_to_graph

# Alice invited Bob and Chloe. Bob invited Chloe twice.
df = pd.DataFrame({
  "hosts": ["Alice", "Alice", "Bob", "Bob"],
  "guests": ["Bob", "Chloe", "Chloe", "Chloe"]
})

g = edges_table_to_graph(
  df,
  edge_source_col="hosts",
  edge_target_col="guests",
  count_rows_as_weight=True,
  directed=True
)

Sigma(g, edge_size='weight')

Using pelote again, you can also create a bipartite network (students and their professors, for example) with the table_to_bipartite_graph function:

import pandas as pd
from pelote import table_to_bipartite_graph

df = pd.DataFrame({
  "professor": ["A", "A", "A", "B", "B", "B", "B"],
  "student": ["C", "D", "E", "C", "F", "G", "H"],
})

g = table_to_bipartite_graph(df, 'student', 'professor', node_part_attr='status')

Sigma(g, node_color='status', default_node_size=10, show_all_labels=True)

Comparing two features of a graph

Let's say we have a graph of websites that we categorized by type and language and we want to compare the distribution of those categories on the graph's topology. We could use node color for language and border color for type but you will quickly see that this is probably not readable.

To solve this kind of problems and enable its users to easily compare multiple features of a graph, ipysigma exposes a SigmaGrid widget that arranges multiple synchronized views of the same graph on a grid:

from ipysigma import SigmaGrid

# Views to display can be specified through the `views` kwarg, expecting
# a list of dicts of keyword arguments to give to the underlying Sigma widgets:
SigmaGrid(g, views=[
  {"node_color": "type"},
  {"node_color": "type"}
])

# You can do the same by using the `#.add` method of the grid to
# dynamically add views:
SigmaGrid(g).add(node_color="lang").add(node_color="type")

# Any kwarg passed to the grid directly will be used by all of the views.
# This is useful to avoid repetition:
SigmaGrid(g, node_size=g.degree, views=[
  {"node_color": "type"},
  {"node_color": "type"}
])

# You can of course display more than 2 views
# By default the grid has 2 columns and will wrap to new rows,
# but you can change the number of columns using the `columns` kwarg:
SigmaGrid(g, columns=3, views=[
  {"node_size": g.degree},
  {"node_size": g.in_degree},
  {"node_size": g.out_degree}
])

More examples: functional testing notebooks

If you want comprehensive examples of the widget's visual variables being used, you can read the notebooks found here, which serve as functional tests to the library.

What data can be used as visual variable

There are several ways to specify what you want to use as visual variables (read this for a detailed explanation).

Here is the exhaustive list of what is possible:

Name of a node or edge attribute

# Let's say your nodes have a "lang" attribute, we can use its modalities as values for
# a categorical color palette:
Sigma(g, node_color='lang')

Node or edge mapping

# You can store the data in a mapping, e.g. a dictionary, likewise:
node_lang = {'node1': 'en', 'node2': 'fr', ...}
Sigma(g, node_color=node_lang)

# For edges, the mapping's key must be a 2-tuple containing source & target nodes.
# Note that for undirected graphs, the order of nodes in the tuple
# does not make any difference as both will work.
edge_type = {('node1', 'node2'): 'LIKES', ('node2', 'node3'): 'LOVES'}

Arbitrary iterable

# Any arbitrary iterable such as generators, ranges, numpy vectors,
# pandas series etc. will work. The only requirement is that they should
# follow the order of iteration of nodes or edges in the graph, so we may
# align the data properly.

# Creating a 0 to n generic label for my nodes
Sigma(g, node_label=range(len(g)))

# Random size for my edges
Sigma(g, edge_size=(random() for _ in g.edges))

# Numpy vector
Sigma(g, node_size=np.random.rand(len(g)))

# Pandas series
Sigma(g, edge_size=df.edge_weights)

Partition

# A partition, complete or not, but not overlapping, of nodes or edges:
# Must be a list of lists or a list of sets.
communities = [{2, 3, 6}, {0, 1}, {4, 6}]

Sigma(g, node_color=communities)

networkx/igraph degree view

# Mapping node size on degree is as simple as:
Sigma(g, node_size=g.degree)

igraph VertexClustering

# IGraph community detection / clustering methods return a VertexClustering object
Sigma(g, node_color=g.connected_components())

Sigma(g, node_color=g.community_multilevel())

Arbitrary callable

# Creating a label for my nodes
Sigma(g, node_label=lambda node: 'Label of ' + str(node))

# Using edge weight as size only for some source nodes
Sigma(g, edge_size=lambda u, v, a: attr['weight'] if g.nodes[u]['part'] == 'main' else 1)

# Node callables will be given the following arguments:
#   1. node key
#   2. node attributes

# Edge callables will be given the following arguments:
#  1. source node key
#  2. target node key
#  3. edge attributes

# Note that given callables may choose to take any number of those arguments.
# For instance, the first example only uses the first argument but still works.

Set

# A set will be understood as a binary partition with nodes or edges being
# in it or outside it. This will be mapped to a boolean value, with `True`
# meaning the node or edge was in the partition.

# This will display the nodes 1, 5 and 6 in a color, and all the other ones
# in a different color.
Sigma(g, node_color={1, 5, 6})

Visual variables and kwargs naming rationale

ipysigma lets its users tweak a large number of visual variables. They all work through a similar variety of keyword arguments given to the Sigma widget.

In ipysigma visual variables can be given:

categorical data, which means they will map category values to discrete visualization values such as a node's category being associated with a given color.
continuous data, which means they will map numerical values to a range of sizes or a gradient of colors, like when representing a node's degree by a size on screen.

kwargs naming rationale

To be able to be drawn on screen, every visual variable must use values that have a meaning for the the widget's visual representation. For colors, it might be a HTML color name such as #fa65ea or cyan. For sizes, it might be a number of pixels etc.

If you know what you are doing and want to give ipysigma the same "raw" values as those expected by the visual representation directly, all variables have kwargs starting by raw_, such as raw_node_color.

But if you want ipysigma to map your arbitrary values to a suitable visual representation, all variables have a kwarg without any prefix, for instance node_color.

In which case, if you use categorical data, ipysigma can generate or use palettes to map the category values to e.g. colors on screen. You can always customize the palette or mapping using a kwarg suffixed with _palette or _mapping such as node_color_palette or node_shape_mapping.

And if you use numerical data, then values will be mapped to an output range, usually in pixels, that can be configured with a kwarg suffixed with _range such as node_size_range. Similarly, if you want to map numerical data to a gradient of colors, you will find kwarg suffixed with _gradient such as node_color_gradient.

Sometimes, some values might fall out of the represented domain, such as non-numerical values for continuous variables, or categories outside of the colors available in the given palette. In which case there always exists a kwarg prefixed with default_, such as default_node_color. A neat trick is also to use those kwargs as a way to indicate a constant value if you want all your edges to have the same color for instance, or your nodes to have the same size in pixels.

Finally, it's usually possible to tweak the way numerical values will be mapped from their original domain to the visual one. This is what you do, for instance, when you choose to use a logarithmic scale on a chart to better visualize a specific distribution. Similarly, relevant ipysigma visual variables give access to a kwarg suffixed _scale, such as node_color_scale that lets you easily switch from a linear to a logarithmic or power scale etc. (for more information about this, check this in the next part of the documentation).

To summarize, let's finish with two exhaustive examples: node color & node size.

Categorical or continuous variable: node color as an example

node_color: this kwarg expects some arbitrary values related to your nodes. Those values can be given in multiple ways listed here. By default, node_color is a categorical variable. Hence, given values will be mapped to suitable colors, from a palette generated automatically for you. If you want your data to be interpreted as continuous instead, you will need to give a gradient to the variable through node_color_gradient.
raw_node_color: this kwarg does not expect arbitrary values but CSS colors instead. This way you can always regain full control on the colors you want for your nodes if none of ipysigma utilities suit your particular use-case.
default_node_color: the default_ kwargs always expect a value that will be used in the final representation, so here a CSS color, that will be used if a node category is not found in the color palette or if a node value is not numerical and we are using a gradient.
node_color_palette: by default, ipysigma uses iwanthue to automatically generate fitting color palettes for the categories present in the given data. But sometimes you might want to customize the colors used. In which case this kwarg expects either a dictionary mapping category values to a CSS color such as {'en': 'blue, 'fr': 'red'} or the name of a categorical color scheme from d3-scale-chromatic such as Tableau10 or RdYlBu for instance.
node_color_gradient: if you want to use a color gradient for your node to represent continuous data, you will need to give this kwarg either a 2-tuple containing the "lowest" and "highest" color such as ("yellow", "red") or the name of a continuous color gradient from d3-scale-chromatic such as Inferno or YlGn for instance.
node_color_scale: finally, if you gave a gradient to node_color_gradient and want to apply a nonlinear scale to the given data, you can pass the name of the scale to use such as log or a 2-tuple containing the name of the scale and an optional param such as the scale's base in the case of a logarithmic scale. Here is a binary log scale for instance: ("log", 2).

Continuous variable: node size as an example

node_size: this kwarg expects some arbitrary numerical values related to your nodes. Those values can be given in multiple ways listed here. Then they will be mapped using a scale given to node_size_scale to a range in pixels given to node_size_range before being used on screen.
raw_node_size: if you want to bypass the scale and the range altogether, this kwarg directly takes values to be considered as pixels on screen.
default_node_size: if no relevant value can be found for a node, or if said value is not a valid number, the widget will use this size, expressed in pixels, instead.
node_size_scale: if you want to apply a nonlinear scale to the given data, you can pass the name of the scale to use such as log or a 2-tuple containing the name of the scale and an optional param such as the scale's base in the case of a logarithmic scale. Here is a binary log scale for instance: ("log", 2).
node_size_range: this kwarg lets you customize the output range in pixels we should map the node numerical values to. For instance, if we want to have our nodes to have sizes between 1 pixel and 25 pixels, we would give it (1, 25). Note that most visual variables have a default range and this kwarg can usually be omitted if the defaults suit you.

For a comprehensive view of the available visual variables, the values they expect and how they can be customized, read this next part of the documentation.

Scales, palettes and gradients

Available scales

lin: linear scale, used by default when scale is not specified.
log: logarithmic scale. Takes an optional base (e by default).
log+1: logarithmic scale incrementing your values by one. This is a well-known visualization trick designed to avoid issues with zeros, which is often the case when using some typical node metrics. Takes an optional base (e by default)..
pow: power scale. Takes an optional exponent (2 by default).
sqrt: square root scale (same as power scale but with inverted exponent). Takes an optional exponent (2 by default).

All the _scale kwargs can take the following:

Nothing (the default), then the scale remains linear: node_size_scale=None.
The name of the scale directly: node_size_scale="log".
A 2-tuple containing the name of the scale and its parameter: node_size_scale=("log", 2).

Color palettes

By default, color palettes are generated for you by ipysigma using iwanthue. ipysigma will first count the number of distinct categories to represent, sort them by frequency and generate a palette of up to 10 colors for the most used ones. The other one will use the default one given to the relevant default_ kwarg such as default_node_color for instance.

Note that this maximum number of 10 can be increased using the max_categorical_colors kwarg.

Note also that the palette generation is seeded using the mapped attribute name in the data so that the palette is always the same (if the name and the category count remains the same), but is different from one attribute to the other.

If you don't want ipysigma to generate color palettes for you, you can give your own palette through the relevant _palette kwarg such as node_color_palette, or use some d3-scale-chromatic one (they have names starting with scheme).

Here is the full list of those palettes supported by ipysigma: Accent, Blues, BrBG, BuGn, BuPu, Category10, Dark2, GnBu, Greens, Greys, OrRd, Oranges, PRGn, Paired, Pastel1, Pastel2, PiYG, PuBu, PuBuGn, PuOr, PuRd, Purples, RdBu, RdGy, RdPu, RdYlBu, RdYlGn, Reds, Set1, Set2, Set3, Spectral, Tableau10, YlGn, YlGnBu, YlOrBr, YlOrRd.

Color gradients

Color gradients can be defined as a range from "lowest" to "highest" color, e.g. ("yellow", "red).

They can also be taken from any d3-scale-chromatic continuous gradient (they have names starting with interpolate).

Here is the full list of those gradients supported by ipysigma: Blues, BrBG, BuGn, BuPu, Cividis, Cool, CubehelixDefault, GnBu, Greens, Greys, Inferno, Magma, OrRd, Oranges, PRGn, PiYG, Plasma, PuBu, PuBuGn, PuOr, PuRd, Purples, Rainbow, RdBu, RdGy, RdPu, RdYlBu, RdYlGn, Reds, Sinebow, Spectral, Turbo, Viridis, Warm, YlGn, YlGnBu, YlOrBr, YlOrRd.

Widget-side metrics

Since ipysigma is using graphology, it can also draw from its library of graph theory metrics.

As such, the node_metrics enables you to ask your widget to compute node metrics on its own and use to map the result on any visual variable.

Here is how you can specify metrics to be computed:

# node_metrics expects an iterable of metrics to compute:
Sigma(g, node_metrics=["louvain"], node_color="louvain")

# They can be specified by name, but you can also specify through
# a dictionary if you need parameters for the metrics:
Sigma(g, node_metrics=[{"name": "louvain", "resolution": 1.5}], node_color="louvain")

# You can also give a dictionary mapping resulting attribute name to
# the metric to compute if you don't want to map the result on an attribute
# having the same name as the metric:
Sigma(g, node_metrics={"community": "louvain"}, node_color="community")
Sigma(g, node_metrics={"community": {"name": "louvain", "resolution": 1.5}}, node_color="community")

Available node metrics & their parameters

louvain: Louvain algorithm for community detection (through modularity optimization)
- resolution ?float [1]: resolution parameter.

Frequently asked questions

Why are there so few labels displayed?

Labels are costly to render and can negate the benefit of using a WebGL renderer such as sigma.js to render interactive graphs. As such, sigma.js relies on a constant size grid to select the "worthiest" labels to display, after taking camera zoom into account.

You can tweak the parameters of this grid using label_grid_cell_size and label_density. Decreasing the first one or increasing the second one will result in more labels being displayed.

Also, by default, the label of a node is displayed only if its size in pixels is larger than a threshold. You can change that threshold using the label_rendered_size_threshold kwarg.

Finally, if you don't want to deal with all this nonsense and just want to display all labels because you know what you are doing and don't care about performance, you can just use show_all_labels=True instead.

Why are some of my categories mapped to a dull grey?

When ipysigma generates palettes for you, it only uses up to 10 colors by default. This number can be increased using the max_categorical_colors kwarg. For more information about palette generation, read this part of the documentation.

Some designer told me (while holding a baseball bat) that it is unwise to have more than 10 categorical colors because you won't be able to distinguish them anymore. My hands are tied. Don't ask me to change this.

I gave colors to node_color but arbitrary colors are displayed by the widget instead

node_color does not expect colors per se but arbitrary data that will be mapped to a suitable color palette for you. If you want to give colors directly, use raw_node_color instead. For more information about the visual variables kwarg naming rationale, read this part of the documentation.

My computer sounds like an airplane taking off

Don't forget to turn off the layout when it has converged (the pause button on the left). There is no convincing way to automatically detect when layout has converged so we must rely on you, the user, to indicate when it's done.

If you want to start the layout automatically when instantiating the widget and make sure it will automatically stop after, say, 10 seconds, use start_layout=10.

Some of my widgets only display labels or a glitchy black box

Your GPU can only render so many webgl canvases in your browser tabs. So if you created too many widgets (this depends on the specifics of your computer and graphics card), it may gracefully deal with the situation by erasing the graph (but not the labels since those are rendered using 2d canvases) or by glitching to death.

My graph is ugly, make it beautiful like Gephi

Note that this border size will be added to the node's radius.

Related kwargs

node_border_size
raw_node_border_size
default_node_border_size
node_border_size_range

Notes

Borders are only shown on screen if a node_border_size OR a node_border_ratio AND a node_border_color are defined.

node_border_ratio

Type

Continuous.

Raw values

A border ratio, in percentage, with default camera (not zoomed nor unzoomed).

Note that this border ratio will eat the node's size.

Related kwargs

node_border_ratio
raw_node_border_ratio
default_node_border_ratio
node_border_ratio_range

Notes

Borders are only shown on screen if a node_border_size OR a node_border_ratio AND a node_border_color are defined.

node_border_color

Type

Categorical or continuous.

Raw values

HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)

Related kwargs

node_border_color
raw_node_border_color
default_node_border_color
node_border_color_palette
node_border_color_gradient
node_border_color_scale

Notes

Borders are only shown on screen if a node_border_size OR a node_border_ratio AND a node_border_color are defined.

node_pictogram

Type

Categorical.

Raw values

The name of any Google Material Icon as listed here (the name must be lowercase and snake_case, e.g. the name "Arrow Drop Done" should be given to ipysigma as arrow_drop_done).

Alternatively, one can also give urls of publicly accessible svg icons such as https://fonts.gstatic.com/s/i/short-term/release/materialsymbolsoutlined/arrow_drop_down/default/48px.svg

Related kwargs

raw_node_pictogram
default_node_pictogram

Notes

Pictograms are only shown on screen if node_pictogram AND node_pictogram_color are defined.

node_pictogram_color

Type

Categorical.

Raw values

HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)

Related kwargs

node_pictogram_color
raw_node_pictogram_color
default_node_pictogram_color
node_pictogram_color_palette

Notes

Pictograms are only shown on screen if node_pictogram AND node_pictogram_color are defined.

node_shape

Type

Categorical.

Raw values

The name of a supported shape such as: circle, triangle, square, pentagon, star, hexagon, heart or cloud.

Alternatively, if you are feeling adventurous, it can also be the name of any Google Material Icon as listed here (the name must be lowercase and snake_case, e.g. the name "Arrow Drop Done" should be given to ipysigma as arrow_drop_done).

Finally, one can also give urls of publicly accessible svg icons such as https://fonts.gstatic.com/s/i/short-term/release/materialsymbolsoutlined/arrow_drop_down/default/48px.svg

Related kwargs

node_shape
raw_node_shape
default_node_shape
node_shape_mapping

Note

Node shapes cannot be used with borders nor pictograms nor halos, as of yet.

node_halo_size

Type

Continuous.

Raw values

A halo size offset in pixels, with default camera (not zoomed nor unzoomed). The full halo radius will therefore be its size + its node's radius.

Related kwargs

node_halo_size
raw_node_halo_size
default_node_halo_size
node_halo_size_range
node_halo_size_scale

node_halo_color

Type

Categorical or continuous.

Raw values

HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)

Related kwargs

node_halo_color
raw_node_halo_color
default_node_halo_color
node_halo_color_palette
node_halo_color_gradient
node_halo_color_scale

edge_color

Type

Categorical or continuous.

Raw values

HTML named color or hex color or rgb/rgba color. Examples: red, #fff, #a89971, rgb(25, 25, 25), rgba(25, 145, 56, 0.5)

Related kwargs

edge_color
raw_edge_color
default_edge_color
edge_color_palette
edge_color_gradient
edge_color_scale

edge_type

Type

Name of renderer to use to draw the graph's edges. One of arrow, triangle, rectangle, line or curve.

Usually defaults to rectangle for undirected graphs and arrow for directed graphs, or curve if edge_curveness is activated.

It cannot be mapped to some edge attribute or data as of yet.

Related_kwargs

default_edge_type

edge_size

Type

Continuous.

Raw values

An edge thickness in pixels, with default camera (not zoomed nor unzoomed).

Related kwargs

edge_size
raw_edge_size
default_edge_size
edge_size_range
edge_size_scale

edge_curveness

Type

Continuous.

Raw values

A percentage. Note that it can go beyond 1 and that 0 will make the edge disappear.

Related kwargs

default_edge_curveness

edge_label

Type

Raw only.

Raw values

A text label.

Related kwargs

edge_label
raw_edge_label
default_edge_label

API Reference

Sigma

Arguments

graph nx.AnyGraph or ig.AnyGraph - networkx or igraph graph instance to explore.
name str, optional None - name of the graph.
height int, optional 500 - height of the widget container in pixels.
background_color str, optional "white" - css color to use as the graph's background.
raw_height str, optional None - raw css height. Can be useful in some html embedding scenarios. Only use this if you know what you are doing.
start_layout bool or float, optional False - whether to automatically start the layout algorithm when mounting the widget. If a number is given instead, the layout algorithm will start and automatically stop after this many seconds.
node_metrics Iterable or Mapping, optional None - node metrics to be computed by graphology by the widget's JavaScript code. Currently only supports "louvain" for community detection.
layout_settings dict, optional None - settings for the ForceAtlas2 layout (listed here: https://graphology.github.io/standard-library/layout-forceatlas2#settings.
clickable_edges bool, optional False - whether to allow user to click on edges to display their information. This can have a performance cost on larger graphs.
process_gexf_viz bool, optional True - whether to process gexf files viz data for node & edges.
max_categorical_colors int, optional 10 - max number of colors to be generated for a categorical palette. Categories, ordered by frequency, over this maximum will use the default color.
hide_info_panel bool, optional False - whether to hide the information panel to the right of the widget.
hide_search bool, optional False - whether to hide the search bar to the right of the widget.
hide_edges_on_move bool, optional False - whether to hide the edges when the graph is being moved. This can be useful to improve performance when the graph is too large.
sync_key str, optional - Key used by the widget to synchronize events between multiple instances of views of a same graph. Prefer using SigmaGrid when able, it will handle this advanced aspect of the widget for you.
sync_targets Iterable, optional ("layout", "camera", "selection", "hover") - Names of targets to synchronize through the sync_key kwarg. Targets include "layout", "camera", "selection" and "hover".
camera_state dict, optional {"x": 0.5, "y": 0.5, "ratio": 1, "angle": 0} - Initial state for the widget's camera (which can be retrieved using the #.get_camera_state method).
selected_node str or int, optional None - Key of the initially selected node in the widget (can be retrieved using the #.get_selected_node method).
selected_edge tuple, optional None - (source, target) tuple of the initially selected edge in the widget (can be retrieved using the #.get_selected_edge method).
selected_node_category_values Iterable, optional None - list of selected node category values (can be retrieved using the #.get_selected_node_category_values method).
selected_edge_category_values Iterable, optional None - list of selected edge category values (can be retrieved using the #.get_selected_edge_category_values method).
label_font str, optional "sans-serif" - font to be used with labels.
label_density int, optional 1 - number of labels to display per grid cell for default camera zoom.
label_grid_cell_size int, optional 250 - size in pixels of a square cell in the label selection grid.
label_rendered_size_threshold int, optional None - minimum actual rendered size (after camera zoom operations) a node must have on screen for its label to be allowed to be displayed. If None, the threshold will be inferred based on the maximum node size of your graph.
show_all_labels bool, optional False - macro setting making sure most, if not all, labels get displayed on screen. Might have an impact on performance with larger graphs.
layout Mapping, optional None - node positions, expressed as a {node: {x, y} mapping.
node_color VariableData, optional None - data to be used as categorical or continuous node color.
raw_node_color VariableData, optional "color" - raw data (css colors) to be used for node colors.
node_color_gradient Iterable or str, optional None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, node color will be interpreted as continuous rather than categorical.
node_color_scale tuple or str, optional None - scale to use for node color. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.
node_color_palette Mapping or str, optional None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).
default_node_color str, optional "#999" - default color for nodes.
node_color_saturation VariableData, optional None - data to be used as continuous node color saturation.
raw_node_color_saturation VariableData, optional None - raw data (percentage) to be used for node color saturation.
node_color_saturation_scale tuple or str, optional None - scale to use for node color saturation. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.
node_color_saturation_range Iterable, optional (0, 1) - range of percentages to map to, for instance: (0, 0.7).
default_node_color_saturation str, optional None - default color saturation for nodes.
node_border_color VariableData, optional None - data to be used as categorical or continuous node border color.
raw_node_border_color VariableData, optional "color" - raw data (css colors) to be used for node border colors.
node_border_color_gradient Iterable or str, optional None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, node border color will be interpreted as continuous rather than categorical.
node_border_color_palette Mapping or str, optional None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).
node_border_color_from str, optional None - optionally select node border color from the following options: "node".
default_node_border_color str, optional - default color for node borders.
node_border_size VariableData, optional None - data to be used as continuous node border size.
raw_node_border_size VariableData, optional None - raw data (size in pixels) to be used for node border sizes.
node_border_size_range Iterable, optional (1, 5) - range of sizes in pixels to map to, for instance: (1, 15).
default_node_border_size int or float, optional 1 - default size for node borders.
node_border_ratio VariableData, optional None - data to be used as continuous node border ratio.
raw_node_border_ratio VariableData, optional None - raw data (ratio in pixels) to be used for node border ratios.
node_border_ratio_range Iterable, optional (0.1, 0.5) - range of ratios in pixels to map to, for instance: (1, 15).
default_node_border_ratio int or float, optional 0.1 - default ratio for node borders.
raw_node_pictogram VariableData, optional None - raw data (pictogram name, as found here: https://fonts.google.com/icons or publicly accessible svg icon url) to be used for node pictograms.
default_node_pictogram str, optional None - default pictogram for nodes.
node_pictogram_color VariableData, optional None - data to be used as categorical or continuous node pictogram color.
raw_node_pictogram_color VariableData, optional "color" - raw data (css colors) to be used for node pictogram colors.
node_pictogram_color_palette Mapping or str, optional None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).
default_node_pictogram_color str, optional "black" - default color for node pictograms.
node_shape VariableData, optional None - data to be used as categorical data to be mapped to node shapes.
raw_node_shape VariableData, optional None - raw data (shape name, or pictogram name as found here: https://fonts.google.com/icons or publicly accessible svg icon url) to be used as node shapes.
node_shape_mapping Mapping, optional - mapping from category values to node shapes.
default_node_shape str, optional None - default shape for nodes.
node_halo_color VariableData, optional None - data to be used as categorical or continuous node halo color.
raw_node_halo_color VariableData, optional "color" - raw data (css colors) to be used for node halo colors.
node_halo_color_gradient Iterable or str, optional None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, node halo color will be interpreted as continuous rather than categorical.
node_halo_color_palette Mapping or str, optional None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).
default_node_halo_color str, optional "red" - default color for node halos.
node_halo_size VariableData, optional None - data to be used as continuous node halo size.
raw_node_halo_size VariableData, optional None - raw data (size in pixels) to be used for node halo sizes.
node_halo_size_range Iterable, optional (0, 20) - range of sizes in pixels to map to, for instance: (1, 15).
default_node_halo_size int or float, optional 0 - default size for node halos.
node_size VariableData, optional "size" - data to be used as continuous node size.
raw_node_size VariableData, optional None - raw data (size in pixels) to be used for node sizes.
node_size_range Iterable, optional (3, 15) - range of sizes in pixels to map to, for instance: (1, 15).
node_size_scale tuple or str, optional None - scale to use for node size. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.
default_node_size int or float, optional None - default size for nodes.
node_label VariableData, optional None - data to be used as node label.
raw_node_label VariableData, optional "label" - raw data (label string) to be used for node labels.
default_node_label str, optional None - default label for nodes.
node_label_size VariableData, optional None - data to be used as continuous node label size.
raw_node_label_size VariableData, optional None - raw data (size in pixels) to be used for node label sizes.
node_label_size_range Iterable, optional (8, 25) - range of sizes in pixels to map to, for instance: (1, 15).
default_node_label_size int or float, optional 12 - default size for node labels.
node_label_color VariableData, optional None - data to be used as categorical or continuous node label color.
raw_node_label_color VariableData, optional None - raw data (css colors) to be used for node label colors.
node_label_color_palette Mapping or str, optional None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).
default_node_label_color str, optional "black" - default color for node labels.
node_zindex VariableData, optional None - numerical data used to sort nodes before rendering. Nodes having a higher zindex will be drawn on top of nodes having a lower one.
edge_color VariableData, optional None - data to be used as categorical or continuous edge color.
raw_edge_color VariableData, optional "color" - raw data (css colors) to be used for edge colors.
edge_color_gradient Iterable or str, optional None - gradient of colors to map to, for instance: (("yellow", "red")), or name of a d3 continuous color scale (found here: https://github.com/d3/d3-scale-chromatic#readme), for instance: "Viridis". If given, edge color will be interpreted as continuous rather than categorical.
edge_color_scale tuple or str, optional None - scale to use for edge color. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.
edge_color_palette Mapping or str, optional None - either a mapping from category values to css colors or the name of a d3 categorical color scale (found here: https://github.com/d3/d3-scale-chromatic#readme).
default_edge_color str, optional "#999" - default color for edges.
default_edge_type str, optional None - default type used to draw edges. Can be selected from "rectangle", "line", "curve", "arrow" & "triangle". Will raise if "arrow" or "triangle" is selected with an undirected graph. If None, will default to "rectangle".
edge_size VariableData, optional "size" - data to be used as continuous edge size.
raw_edge_size VariableData, optional None - raw data (size in pixels) to be used for edge sizes.
edge_size_range Iterable, optional (3, 15) - range of sizes in pixels to map to, for instance: (1, 15).
edge_size_scale tuple or str, optional None - scale to use for edge size. Can be a tuple containing the name of the scale and an additional param such as an exponent, or just the name of the scale to use: e.g. ("log", 2) or "pow". Available scales include: "lin", "log", "log+1", "pow" & "sqrt". If None is given, scale will default to "lin" for linear.
default_edge_size int or float, optional None - default size for edges.
default_edge_curveness str, optional 0.25 - curveness factor for edges when default_edge_type is "curve".
edge_label VariableData, optional None - data to be used as edge label.
raw_edge_label VariableData, optional "label" - raw data (label string) to be used for edge labels.
edge_weight VariableData, optional - numerical data to be used as edge weight for weighted metrics & layout computations (distinct from size, which is used for rendering).
edge_zindex VariableData, optional None - numerical data used to sort egdes before rendering. Egdes having a higher zindex will be drawn on top of egdes having a lower one.

#.get_layout

Method returning the layout of the graph, i.e. the current node positions in the widget, as a dict mapping nodes to their {x, y} coordinates.

#.get_camera_state

Method returning the current camera state of the widget, as a {x, y, ratio, angle} dict.

#.get_selected_node

Method returning the currently selected node if any or None.

#.get_selected_edge

Method returning the currently selected edge as a (source, target) tuple if any or None.

#.get_selected_node_category_values

Method returning a set of currently selected node category values or None.

#.get_selected_edge_category_values

Method returning a set of currently selected edge category values or None.

#.render_snapshot

Method rendering the widget as an rasterized image in the resulting cell.

#.to_html

Method rendering the widget as a standalone HTML file that can be hosted statically elsewhere.

Arguments

path PathLike or file: where to save the HTML file.

Sigma.set_defaults

Static method that can be used to override some default values of the Sigma class kwargs.

Arguments

height int, optional: default widget height in pixels.
background_color str, optional: default background color.
max_categorical_colors int, optional: default maximum number of colors for generated palettes.
node_size_range tuple, optional: default size range in pixels for nodes.
edge_size_range tuple, optional: default size range in pixels for edges.

Sigma.write_html

Static method taking the same kwargs as Sigma and rendering the widget as a standalone HTML file that can be hosted statically elsewhere.

Arguments

graph nx.AnyGraph or ig.AnyGraph: graph to represent.
path PathLike or file: where to save the HTML file.
fullscreen bool, optional [False]: whether to display the widget by taking up the full space of the screen. If False, will follow the given height.
**kwarg: any kwarg accepted by Sigma.

SigmaGrid

Arguments

graph nx.AnyGraph or ig.AnyGraph - networkx or igraph graph instance to visualize.
columns int, optional 2 - maximum number of views to display in a line.
sync_key str, otpional None - synchronization key to use. If not given, one will be automatically generated by the grid.
views list, optional None - list of kwarg dicts that will be used to instantiate the underlying Sigma views as an alternative to using the #.add method.
**kwargs - any other kwarg will be passed as-is to Sigma views.

#.add

Method one can use as an alternative or combined to SigmaGrid constructor's views kwarg to add a new Sigma view to the grid. It takes any argument taken by Sigma and returns self for easy chaining.

SigmaGrid(g, node_color='category').add(node_size=g.degree).add(node_size='occurrences')

How to cite

Guillaume Plique. (2022). ipysigma, A Jupyter widget using sigma.js to render interactive networks. Zenodo. https://doi.org/10.5281/zenodo.7446059